Navigating the Transformative Impact of Artificial Intelligence in Health Services Research

被引:0
作者
Wu, Guosong [1 ,2 ]
Yang, Fengjuan [2 ]
机构
[1] Cape Breton Univ, Shannon Sch Business, Dept Hlth Care Analyt, Sydney, NS, Canada
[2] Univ Calgary, Ctr Hlth Informat, Cumming Sch Med, Calgary, AB, Canada
关键词
artificial intelligence; health services research; quality of health care; AI;
D O I
10.1002/hsr2.70793
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
Background and AimsArtificial intelligence (AI) is transforming health services research by providing novel insights, enhancing care quality, and improving patient outcomes. This review sought to assess AI's impact on health services research, highlighting its applications, benefits, and associated challenges.MethodsWe conducted a comprehensive review of recent literature on AI applications in health services research. Key areas of focus included image processing and language processing. The review also addressed the ethical and practical challenges of integrating AI into healthcare.ResultsOver the past decade, AI-related research has markedly increased. AI has significantly advanced health services research by improving diagnostic precision, care quality, decision-making, hospital operations, and personalized care. The benefits of AI in image processing and language processing have been substantial, resulting in positive impacts on healthcare practices. However, integrating AI into healthcare presents considerable ethical and practical challenges, including the need for robust data security, the mitigation of algorithmic biases, and the achievement of interoperability among diverse data systems.ConclusionsAI offers significant potential to advance health services research and enhance patient care through powerful applications in image processing, language processing, diagnostic precision, decision-making, and hospital operations. By leveraging AI's capabilities, healthcare systems can achieve more personalized, efficient, and accurate care. Addressing key challenges is essential for the effective and equitable integration of AI into healthcare systems.
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页数:5
相关论文
共 47 条
[11]   Artificial intelligence in digital pathology - new tools for diagnosis and precision oncology [J].
Bera, Kaustav ;
Schalper, Kurt A. ;
Rimm, David L. ;
Velcheti, Vamsidhar ;
Madabhushi, Anant .
NATURE REVIEWS CLINICAL ONCOLOGY, 2019, 16 (11) :703-715
[12]   Ethical, legal, and social considerations of AI-based medical decision-support tools: A scoping review [J].
Cartolovni, Anto ;
Tomicic, Ana ;
Mosler, Elvira Lazic .
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS, 2022, 161
[13]   Can we open the black box of AI? [J].
Castelvecchi D. .
Nature, 2016, 538 (7623) :20-23
[14]   PRINCIPLES OF SOCIOTECHNICAL DESIGN [J].
CHERNS, A .
HUMAN RELATIONS, 1976, 29 (08) :783-792
[16]   AI-based clinical decision-making systems in palliative medicine: ethical challenges [J].
De Panfilis, Ludovica ;
Peruselli, Carlo ;
Tanzi, Silvia ;
Botrugno, Carlo .
BMJ SUPPORTIVE & PALLIATIVE CARE, 2023, 13 (02) :183-189
[17]   AI Technologies, Privacy, and Security [J].
Elliott, David ;
Soifer, Eldon .
FRONTIERS IN ARTIFICIAL INTELLIGENCE, 2022, 5
[18]   Dermatologist-level classification of skin cancer with deep neural networks [J].
Esteva, Andre ;
Kuprel, Brett ;
Novoa, Roberto A. ;
Ko, Justin ;
Swetter, Susan M. ;
Blau, Helen M. ;
Thrun, Sebastian .
NATURE, 2017, 542 (7639) :115-+
[19]   Use of artificial intelligence for image analysis in breast cancer screening programmes: systematic review of test accuracy [J].
Freeman, Karoline ;
Geppert, Julia ;
Stinton, Chris ;
Todkill, Daniel ;
Johnson, Samantha ;
Clarke, Aileen ;
Taylor-Phillips, Sian .
BMJ-BRITISH MEDICAL JOURNAL, 2021, 374
[20]  
Ghassemi M, 2019, Arxiv, DOI arXiv:1806.00388